Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	Louisiana	37,714
2	North Dakota	37,070
3	Mississippi	35,596
4	Florida	34,395
5	Alabama	34,098
6	South Dakota	33,164
7	Iowa	32,180
8	Tennessee	31,544
9	Arkansas	31,203
10	Arizona	31,152
11	South Carolina	30,858
12	Georgia	29,956
13	Texas	29,039
14	Nevada	28,235
15	Wisconsin	28,190
16	Idaho	27,971
17	Nebraska	27,679
18	Utah	27,392
19	Illinois	25,975
20	Oklahoma	25,649
21	Rhode Island	25,449
22	New York	24,747
23	Missouri	24,521
24	New Jersey	24,416
25	Kansas	23,796
26	Delaware	22,997
27	District of Columbia	22,767
28	North Carolina	22,391
29	Maryland	21,972
30	California	21,856
31	Indiana	20,870
32	Massachusetts	20,371
33	Minnesota	20,325
34	Virginia	18,839
35	Kentucky	18,794
36	Montana	18,415
37	Connecticut	17,304
38	Puerto Rico	17,077
39	New Mexico	16,078
40	Michigan	15,304
41	Alaska	14,981
42	Ohio	14,682
43	Pennsylvania	14,034
44	Colorado	13,963
45	Wyoming	13,760
46	Washington	13,041
47	West Virginia	10,353
48	Hawaii	9,701
49	Oregon	8,959
50	New Hampshire	6,824
51	Maine	4,299
52	Vermont	3,022

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	North Dakota	707
2	South Dakota	524
3	Montana	487
4	Wisconsin	480
5	Idaho	384
6	Utah	324
7	Wyoming	293
8	Nebraska	285
9	Tennessee	284
10	Alaska	277
11	Iowa	256
12	Kansas	252
13	Oklahoma	241
14	Illinois	233
15	Indiana	233
16	Minnesota	221
17	Arkansas	215
18	Rhode Island	209
19	Alabama	181
20	Kentucky	174
21	Nevada	163
22	New Mexico	157
23	Connecticut	155
24	North Carolina	155
25	Florida	152
26	South Carolina	146
27	Mississippi	145
28	Texas	145
29	Colorado	137
30	Louisiana	137
31	Delaware	135
32	Missouri	124
33	Puerto Rico	122
34	West Virginia	119
35	Ohio	118
36	Michigan	114
37	Virginia	113
38	Georgia	104
39	Massachusetts	100
40	Pennsylvania	100
41	Maryland	92
42	Washington	91
43	New Jersey	82
44	Arizona	80
45	California	78
46	District of Columbia	70
47	Oregon	68
48	New York	61
49	Hawaii	48
50	New Hampshire	45
51	Maine	20
52	Vermont	15

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,821
2	New York	1,691
3	Massachusetts	1,397
4	Connecticut	1,271
5	Louisiana	1,221
6	Rhode Island	1,075
7	Mississippi	1,046
8	District of Columbia	902
9	Arizona	792
10	Illinois	732
11	Michigan	726
12	Florida	723
13	South Carolina	694
14	Georgia	685
15	Delaware	676
16	Maryland	663
17	Pennsylvania	660
18	Texas	591
19	Indiana	567
20	Nevada	544
21	Alabama	543
22	Arkansas	533
23	North Dakota	475
24	Iowa	472
25	New Mexico	437
26	Ohio	429
27	California	421
28	Missouri	406
29	Tennessee	405
30	Virginia	395
31	Minnesota	390
32	Colorado	378
33	North Carolina	366
34	New Hampshire	335
35	South Dakota	325
36	Washington	303
37	Kentucky	295
38	Idaho	287
39	Oklahoma	282
40	Nebraska	275
41	Kansas	269
42	Wisconsin	261
43	Puerto Rico	231
44	West Virginia	215
45	Montana	203
46	Utah	163
47	Oregon	143
48	Hawaii	121
49	Maine	106
50	Wyoming	98
51	Vermont	92
52	Alaska	76

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	North Dakota	9
2	Arkansas	6
3	Florida	5
4	Colorado	3
5	Iowa	3
6	Louisiana	3
7	Delaware	2
8	Kansas	2
9	Massachusetts	2
10	Mississippi	2
11	Montana	2
12	North Carolina	2
13	Oklahoma	2
14	Rhode Island	2
15	Wisconsin	2
16	Georgia	1
17	Hawaii	1
18	Idaho	1
19	Illinois	1
20	Indiana	1
21	Kentucky	1
22	Michigan	1
23	Minnesota	1
24	Nebraska	1
25	Nevada	1
26	New Mexico	1
27	Puerto Rico	1
28	South Carolina	1
29	Tennessee	1
30	Texas	1
31	Utah	1
32	West Virginia	1
33	Wyoming	1
34	Alabama	0
35	Alaska	0
36	Arizona	0
37	California	0
38	Connecticut	0
39	District of Columbia	0
40	Maine	0
41	Maryland	0
42	Missouri	0
43	New Hampshire	0
44	New Jersey	0
45	New York	0
46	Ohio	0
47	Oregon	0
48	Pennsylvania	0
49	South Dakota	0
50	Vermont	0
51	Virginia	0
52	Washington	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Lincoln	Arkansas	165,080	1	99
Chattahoochee	Georgia	156,413	2	99
Trousdale	Tennessee	151,896	3	99
Lafayette	Florida	148,540	4	99
Lake	Tennessee	135,547	5	99
Davidson	Tennessee	44,413	226	92
Richland	South Carolina	38,979	339	89
York	South Carolina	21,290	1329	57
Orange	California	18,192	1608	48
Pierce	Washington	10,913	2365	24

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	5,203	1	99
Kenedy	Texas	4,950	2	99
Emporia city	Virginia	4,863	3	99
Galax city	Virginia	4,727	4	99
Randolph	Georgia	4,279	5	99
Richland	South Carolina	621	758	75
Davidson	Tennessee	464	1075	65
Orange	California	422	1175	62
York	South Carolina	310	1475	53
Pierce	Washington	260	1654	47

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.